Data-driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

Data-driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation-free approaches. Numerical results in two case studies involving the course-keeping of a naval destroyer in a high sea state using simulation data at model scale are presented. The proposed methods reveal successful in predicting ship motions both in short-term and medium-term perspectives with accuracy and reduced computational effort, thus enabling further advances in the identification, control, and optimization of ships operating in waves.